Water hyacinth (Eichhornia crassipes (Mart)) detection using satellite image processing and artificial intelligence method

Currently, in many applications, humans are often used to translate satellite images into useful data. Based on the experience level of the humans, the accuracy of the obtained data is varied. This research presents a method that adopts one of the artificial intelligence methods, specifically neural networks, to produce useful data that can be used in decision-making to detect water hyacinth weed. The proposed approach will enable scientists to rapidly recognize the world’s worst aquatic weed, Water Hyacinth. This weed currently creates serious agricultural and navigation problems in Iraq. It affects irrigation, water flow, water use and navigation. Consequently, the proposed approach can enhance in maximizing the economic factor, minimizing CO2 emissions and also minimize land degradation.